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Salary: USD 175,000 - 250,000 / annual
Translucent is an AI-native financial platform built exclusively for healthcare providers. The company was founded in 2024 and is backed by GV, NEA, FPV, and Virtue. It has already been deployed by healthcare organizations managing over $5 billion in combined revenue.
As Subject Matter Expert for Physician Enterprise, you will be the domain authority shaping how Translucent's AI agents reason about physician productivity, compensation design, and access management. Your judgment will directly influence product development, ensuring AI-generated insights meet the standards that working medical group leaders would apply.
Key responsibilities include:
- Develop and deliver subject matter expertise in medical group and employed-physician operations to support AI product development and customer outcomes
- Collaborate with engineering, product, and design teams to define AI systems for physician enterprise workflows
- Build proprietary benchmarks and datasets to evaluate AI agents against real-world tasks: wRVU productivity analysis, benchmark comparison and normalization, compensation plan modeling, advanced practice provider leverage, incident-to and split/shared billing review, and template/access analysis
- Partner with customer delivery teams to understand medical group operations, identify pain points, and translate complex financial and clinical requirements into technical solutions
You bring 7+ years in physician enterprise, medical group operations, or practice administration, with exposure to compensation design, productivity benchmarking, and access management. You have strong proficiency with practice management systems (Epic Cadence or similar), compensation modeling in spreadsheets, and benchmark sources like MGMA. You understand where benchmark comparisons mislead and can convert workflows into structured data and logic for algorithms. You communicate effectively across physicians, product teams, and engineers, and you're willing to do detailed analytical work—grading model outputs provider by provider, auditing encounters, and separating real productivity gaps from coding artifacts. Nice-to-haves include startup experience, familiarity with SQL/Python, and exposure to AI/ML concepts.
